The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

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This report is the outcome of the meeting: “Environmental and Human Health Consequences of Arsenic”, held at the MDI Biological Laboratory in Salisbury Cove, Maine, August 13–15, 2014. Human exposure to arsenic represents a significant health problem worldwide that requires immediate attention according to the World Health Organization (WHO). One billion people are exposed to arsenic in food and more than 200 million people ingest arsenic via drinking water at concentrations greater than international standards. Although the U.S. Environmental Protection Agency (EPA) has set a limit of 10 micrograms per liter (10 μg/L) in public water supplies and the WHO has recommended an upper limit of 10 μg/L, recent studies indicate that these limits are not protective enough. In addition, there are currently few standards for arsenic in food. Those who participated in the Summit support citizens, scientists, policymakers, industry and educators at the local, state, national and international levels to: (1) Establish science-based evidence for setting standards at the local, state, national, and global levels for arsenic in water and food; (2) Work with government agencies to set regulations for arsenic in water and food, to establish and strengthen non-regulatory programs, and to strengthen collaboration among government agencies, NGOs, academia, the private sector, industry and others; (3) Develop novel and cost-effective technologies for identification and reduction of exposure to arsenic in water; (4) Develop novel and cost-effective approaches to reduce arsenic exposure in juice, rice, and other relevant foods, and (5) Develop an Arsenic Education Plan to guide the development of science curricula as well as community outreach and education programs that serve to inform students and consumers about arsenic exposure and engage them in well water testing and development of remediation strategies.